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Online dating ups and downs

The upside of online dating: There’s always a funny story to tell,The Ups and Downs of Online Dating

 · CNN readers shared their ups and downs of online dating. They agree: Expect the unexpected when you're involved with online dating. Some of these matches might lead AdExclusive African Dating Site. Join Today & Meet Your Match! Find Your Dream African Woman. View Photo Profiles. Join blogger.com by Cupid Media · Over Million Members · Backed by Cupid MediaService catalog: Instant Messaging, Send Interest, Make Connections AdCompare Online Dating Sites, Join the Right Site For You & Meet Singles Online! Compare Dating Sites with Genuine Profiles. Meet Local Singles & Find Your blogger.com has been visited by 10K+ users in the past monthService catalog: Video Chat, See Profiles, Find Singles Nearby, Match with Locals ... read more

Similarly, the three dimensions of volitions, territory planning, account-specific planning and effort, all registered strong positive correlations ranging from 0. Overall, the model construction and model refinement results in 75 per cent of the hypothesized relationships being confirmed. This study reveals several important inter-relationships among the key constructs. Specifically, personal stake in a high-stakes setting has a larger positive impact on PAEs than NAEs.

The model also integrates the emotional process entailing a pre-factual stage, that is, anticipatory emotions, with cognitive-based, behavioural volitions. The results show that PAEs do not have any effect on territory planning volition.

Rather, PAEs only positively and strongly cause account-specific volition and effort volition. For NAEs, they have strong dual impacts on territory planning and effort volition, but minimal impact on account-specific volition. Drawing on this context of a high-personal stake setting using an online dating service, I found support for the statements:. effortful volition is a general dimension that captures both PAEs and NAEs, but only NAEs generate more effortful behaviour;. The results show that PAEs are more strongly and positively related to account-specific planning — an important dimension of volition;.

for NAEs that are negative pre-factual emotions that subjects experience, I measured a battery of ten NAEs, namely, angry, frustrated, guilty, ashamed, sad, disappointed, depressed, worried, uncomfortable and fearful.

I found that NAEs do not influence account-specific planning, but strongly influence territory planning. Some of the limitations of this study should be mentioned. First, I situate the model in a high-stakes setting. This potentially means a lower-personal stake service setting may result in different outcomes when PAEs and NAEs are engaged to predict volition.

The choice of a high-stakes context was to generate sufficient anticipatory emotions. The danger of using a low-personal stake context would be the insufficiency of generating anticipatory emotions, and therefore their impacts on the three volitions might be difficult to observe. Second, despite the fact that PLS copes well with a smaller sample size, a larger sample would make the conclusions more convincing.

With proper advertising manipulation, managers can elicit the kind of PAEs and NAEs that have volitional consequences for specific types of volitions, such as territory planning, account-specific planning and effort. Other highly motivating and engaging services, such as online lottery websites, could use advertising to generate PAEs that drive customers to account-specific planning by way of targeting more specific games, or to make customers more careful to work on their highest-priority first.

NAEs generated could result in personal planning by devoting more time to strategic and analytical planning. Thus, for instance, online banking services or social policy makers could highlight the negative impacts of ineffective personal saving tasks, and thus such elicited NAEs as worry and fear would push customers online to engage in more strategic planning behaviour. This proper management of emotions could possibly enhance consumer welfare and social welfare at large.

Nicola, S. Aymerich-Franch, L. Article Google Scholar. Poels, K. html , accessed 11 January Brown, S. and Slocum Jr, J. Rebane, G. Zhu, J. Google Scholar. Bagozzi, R. and Pieters, R. Malone, T.

Croll, E. De Cuyper, N. and De Witte, H. Malhotra, Y. and Kirsch, L. Reuter, T. and Lippke, S. Shim, S. and Warrington, P.

Chiang, K. and Dholakia, R. Huang, P. and Mitra, S. and Nyer, P. Perugini, M. and Conner, M. Ringle, C. and Will, S. de , accessed 2 September Fornell, C. and Larcker, D. Chin, W. in Marcoulides, G. Download references. Shanghai University, Room , Wende Building, Jiading Campus, Shanghai, , China. You can also search for this author in PubMed Google Scholar.

Correspondence to Jiyao Xun. Reprints and Permissions. Xun, J. The ups and downs of online dating: Effects of positive and negative anticipatory emotions on participant volition behaviour.

J Direct Data Digit Mark Pract 16 , 51—60 Download citation. Received : 29 July Revised : 29 July Published : 06 October Issue Date : 01 July Anyone you share the following link with will be able to read this content:.

Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative. Skip to main content. Search SpringerLink Search. Download PDF. Abstract Existing literature on customer emotions devotes much attention to post-consumption emotions, which are feelings elicited towards actual external stimuli.

Introduction With the maturity and pervasiveness of e-business and the operational simplicity of task-based online self-services, online dating nowadays is increasingly becoming a popular pursuit for many consumers.

Hypotheses development Anticipatory emotions arise when a person contemplates the possible consequences of achieving a goal or not and such appraisals of the consequences produce anticipatory emotional responses. Figure 1. Model of anticipatory emotions on volitions. Full size image. Materials and methods I first developed this measurement to fit the context using a pilot study. Table 1 Measurement scale Full size table.

Table 2 PLS quality criteria Full size table. Table 3 Constructs descriptive statistics and correlation matrix Full size table.

Table 4 Standardized structural equation parameter estimates t -values Full size table. Figure 2. Empirical model with outputs. Discussion There are several significant findings from this empirical work. The results show that PAEs are more strongly and positively related to account-specific planning — an important dimension of volition; 3 for NAEs that are negative pre-factual emotions that subjects experience, I measured a battery of ten NAEs, namely, angry, frustrated, guilty, ashamed, sad, disappointed, depressed, worried, uncomfortable and fearful.

JF: What I find concerning is the way a lot of women end up selling ourselves as sexy, and the thing you're putting forward is what you look like. And there's that complaint from men — she shows up and she's completely different from what they expected from her photos, and they act as if they have been radically deceived.

My photos are the best I could find — I look thinner, prettier etc. But I've gone on dates where I didn't even recognise the person from their photos. PS: I've had some rude comments. I remember joking to this guy that I had been a good-looking teenager.

He looked at me, straight-faced, and said, "what happened? On our first date, I didn't really think anything of him. It was fine, but I had no idea of the wondrous depths and inner beauty beneath. Generally, I find it's perfectly pleasant, but there's no spark and you say goodbye and never see them again. JF: That's mostly been my experience, too. Most dates have been fine. We've gone out once or twice, they've been really nice, but something wasn't there.

PS: People believe that you have a better chance of meeting someone online. But during 13 years of online dating, I've had two long-term relationships. Out of a gazillion dates, having looked at I don't even want to know how many profiles. I'm sure that's no better than if you went to the same bar every week for 13 years. JF: It takes some of the serendipity out of it, and that magic when you see somebody in person and feel an attraction. There are plenty of men I have dated in real life who, if I had seen their photo online, I would have probably clicked "next".

The benefit of being online is that you have this enormous pool of people who you know are all single. PS: If you're feeling needy or lonely and you go online hoping for a quick fix of approval or validation and you don't get it, or worse you get people saying "no thanks", that can be quite damaging. It's also a perfect tool for projection. Based on a few facts, you think you have all your criteria ticked, so you project a lot of positive stuff on them, and when you meet them your expectations are too high.

Invariably they disappoint. The number of times I've immediately had that stomach sink of: "Oh, you're really not that person, are you? JF: I've had some positive experiences, which is why I keep doing it. Dating should be hard. Finding someone to spend a large chunk of your life with should be one of the most difficult things you do.

I don't want to end up with somebody who is the easy option. Most of the people I have met haven't been right for me, but they've been nice guys and great fits for someone else. So I see it as pretty heartening. PS: I'm an optimist. My last relationship was very happy and rewarding, and he remains my best friend. And I met him online. There's a little voice in my head that says you can meet another one and maybe this time it will last for ever. Patrick Strudwick's online petition urging Citibank and Barclays to condemn the Ugandan death penalty for gay people can be found at change.

News Opinion Sport Culture Lifestyle Show More Show More News World news UK news Coronavirus Climate crisis Environment Science Global development Football Tech Business Obituaries. This article is more than 9 years old. Interview by Emine Saner.

Existing literature on customer emotions devotes much attention to post-consumption emotions, which are feelings elicited towards actual external stimuli. In this study, I integrate hot and emotional anticipatory emotions with cold and cognitive-based volition processes into a single research model.

I chose to model seven positive anticipatory emotions PAEs and 10 negative anticipatory emotions NAEs on three dimensions of behavioural volition: territory planning, account-specific planning and effort. A sample of 93 real paying members registered on online dating websites in China was employed to test the model empirically. This context entails a high personal stake and exhibits a high level of intrinsically motivating and goal-directed behaviour that appears most suitable to elicit the anticipatory emotions for this study.

Partial least squares structural equation modelling techniques validate the hypotheses and yield some interesting findings regarding the interplay among PAEs, NAEs and three types of volition. With the maturity and pervasiveness of e-business and the operational simplicity of task-based online self-services, online dating nowadays is increasingly becoming a popular pursuit for many consumers. The online dating or matchmaking services industry exhibits great potential with more than 1, sites in operation, 1 where firms such as Match.

com and eHarmony. com generate income worth an estimated £m a year in the UK. Understanding these emotional outcomes in both positive and negative ways and how their respective motivation changes in the context of a dating site would be of great theoretical and practical importance for cyber-psychological study and e-business managers. Managers are struggling to keep their customers motivated: Thomas Enraght-Moony, CEO of Match.

There are 12 million single people in Britain. In this study, I aim to investigate the causal link between anticipatory emotions affective process of cyber-psychology and volitions cognitive process of cyber-behaviour.

This study examines the relationship between the emotions elicited in users when using an e-service and their motivational volition behaviour developed with this e-service provider. Anticipatory emotions arise when a person contemplates the possible consequences of achieving a goal or not and such appraisals of the consequences produce anticipatory emotional responses. Anticipatory emotions can be further divided into positive and negative ones in terms of valence. Positive anticipatory emotions PAEs and negative anticipatory emotions NAEs emerge when such appraisals of the consequences are either positive or negative, respectively.

In this study, I borrow anticipatory emotion measures based on prior research. There is also a battery of ten NAEs, namely, angry, frustrated, guilty, ashamed, sad, disappointed, depressed, worried, uncomfortable and fearful.

The statements of measures are modified accordingly to fit the online dating context. Crucial to anticipatory emotions are the personal stakes vested in an event.

Online daters are typically goal-directed and have to finish a fair amount of nominal user tasks also known as NUTs 6 before starting to advertise their profiles on the site and starting interactions with other registered users.

These processes typically require several effortful submissions of NUTs before proceeding to the next steps. For a registered dating user, there are about six NUTs on average eg, personal information, self-description, lifestyle, expected perfect match, personal ad and photo uploading to be completed before the service provider offers matching suggestions. Vested financial and social interests demonstrate an intrinsic motivated intent. In the psychology literature, volition is used interchangeably with behavioural volition, which is defined as acts of the will, special mental events or activities by which an agent consciously and actively exercises her agency to voluntarily direct her thoughts and actions.

Figure 1 illustrates the model of anticipatory emotions on volitions: a personal stake is the primary antecedent of emotions. For an online dating activity, an initial emotional state is anticipated either PAE or NAE. Volitions have three dimensions. People experience a high level of emotion when they engage in intrinsically motivating activities. For instance, in a sales task, if salespeople see that the task has a high personal stake invested, they relate the stake of the task to their financial income and recognition from colleagues and the company.

For online dating participants, personal stakes positively relate to their a positive anticipatory emotions and b negative anticipatory emotions. Prior research postulates that during the transformation of anticipatory emotions into goal-directed behaviours, volition — a psychological construct — is an important mediator. In the context of a sales task, 13 salespersons have a detailed, layered plan for achieving their task: a sales territory and specific strategies for targeting specific accounts.

For online dating participants, positive anticipatory emotions positively relate to their volitions, including volitions by way of a territory planning, b account-specific planning and c effort. For online dating participants, negative anticipatory emotions positively relate to their volitions, including volitions by way of a territory planning, b account-specific planning and c effort. I first developed this measurement to fit the context using a pilot study. An online dating website was used for its excellent fit into the cyber-psychology context.

First, online dating sites are different from online merchandizing sites, which typically sell standardized commodities. Second, emotion is argued to inherently impact on relationship formation. Therefore, there is a gap in the literature on the online credence context for psychological research. After an initial qualitative pilot study and focus group analysis and preparation of measurement statements, the author launched electronic surveys on surveymonkey.

The survey was written in simplified Chinese as used in mainland China. The design of the survey follows three criteria.

First, the survey should be short and easy to understand. To facilitate this, an introduction with an illustration was used and the use of Chinese language was kept as clear as possible for ease of understanding. Second, it used a real-life online dating forum for respondent recruitment. Third, to encourage a higher completion rate, a small prize draw was offered to those who were willing to leave their email address, and the personal information requested was kept to a minimum eg, only retaining participant demographic data.

Follow-up enquiries to those who left email addresses did not reveal a collective concern about the style and length of the rating scale. I used SmartPLS 2. The reliability of this partial least squares PLS study was assessed by means of composite scale reliability CR and average variance extracted AVE. I also removed self-assurance as a construct from PAEs measured because its loading is under the 0.

This resulted in the measurement model meeting a high PLS quality standard see Table 2. Next, the discriminant validity of the measures was assessed. This follows the rule that a construct should always share more variance with its measures than with other model constructs, 19 and the square root of the AVE should be larger than the inter-correlations of the constructs with the other model constructs.

In H1b, I hypothesized a positive relationship between personal stakes and NAEs. I then tested the interactions among PAEs on three dimensions of behavioural volitions.

H2a has a non-significant β score, therefore not supporting a proposed positive impact of PAEs on territory planning volitions. In contrast, H2b has a β score of 0. The last relationship of PAEs on effort was supported by a strong β score of 0. H3 hypothesizes the positive relationship of NAEs on three dimensions of behavioural volition.

A summary of the hypotheses testing can be found in Table 4. A summary of the empirical model with statistical outputs is presented in Figure 2. There are several significant findings from this empirical work. First, the main argument of the present study is that linking the seemingly unrelated psychological processes, that is, pre-factual, anticipatory emotions, with the three dimensions of behavioural volitions can be very valuable in understanding consumer behavioural outcomes. The addition of PAEs and NAEs into the model for explaining volitional behaviour is critical.

I performed an ad hoc test excluding PAEs and NAEs in the model, by linking stakes directly to three volition constructs. The results show that neither link is statistically significant. This proves the critical mediation effects of PAEs and NAEs in explaining volitions. From a technical modelling perspective, the R -squares for most of the latent endogenous variables scored 15—21 per cent.

The measurement model is solid and theoretically related constructs have clear strong positive correlations. Similarly, the three dimensions of volitions, territory planning, account-specific planning and effort, all registered strong positive correlations ranging from 0. Overall, the model construction and model refinement results in 75 per cent of the hypothesized relationships being confirmed.

This study reveals several important inter-relationships among the key constructs. Specifically, personal stake in a high-stakes setting has a larger positive impact on PAEs than NAEs. The model also integrates the emotional process entailing a pre-factual stage, that is, anticipatory emotions, with cognitive-based, behavioural volitions.

The results show that PAEs do not have any effect on territory planning volition. Rather, PAEs only positively and strongly cause account-specific volition and effort volition.

For NAEs, they have strong dual impacts on territory planning and effort volition, but minimal impact on account-specific volition. Drawing on this context of a high-personal stake setting using an online dating service, I found support for the statements:.

effortful volition is a general dimension that captures both PAEs and NAEs, but only NAEs generate more effortful behaviour;. The results show that PAEs are more strongly and positively related to account-specific planning — an important dimension of volition;. for NAEs that are negative pre-factual emotions that subjects experience, I measured a battery of ten NAEs, namely, angry, frustrated, guilty, ashamed, sad, disappointed, depressed, worried, uncomfortable and fearful.

I found that NAEs do not influence account-specific planning, but strongly influence territory planning. Some of the limitations of this study should be mentioned.

First, I situate the model in a high-stakes setting. This potentially means a lower-personal stake service setting may result in different outcomes when PAEs and NAEs are engaged to predict volition. The choice of a high-stakes context was to generate sufficient anticipatory emotions. The danger of using a low-personal stake context would be the insufficiency of generating anticipatory emotions, and therefore their impacts on the three volitions might be difficult to observe.

Second, despite the fact that PLS copes well with a smaller sample size, a larger sample would make the conclusions more convincing. With proper advertising manipulation, managers can elicit the kind of PAEs and NAEs that have volitional consequences for specific types of volitions, such as territory planning, account-specific planning and effort. Other highly motivating and engaging services, such as online lottery websites, could use advertising to generate PAEs that drive customers to account-specific planning by way of targeting more specific games, or to make customers more careful to work on their highest-priority first.

NAEs generated could result in personal planning by devoting more time to strategic and analytical planning. Thus, for instance, online banking services or social policy makers could highlight the negative impacts of ineffective personal saving tasks, and thus such elicited NAEs as worry and fear would push customers online to engage in more strategic planning behaviour.

This proper management of emotions could possibly enhance consumer welfare and social welfare at large. Nicola, S. Aymerich-Franch, L. Article Google Scholar.

Online dating: the up and downs,Most viewed

AdCompare Online Dating Sites, Join the Right Site For You & Meet Singles Online! Compare Dating Sites with Genuine Profiles. Meet Local Singles & Find Your blogger.com has been visited by 10K+ users in the past monthService catalog: Video Chat, See Profiles, Find Singles Nearby, Match with Locals  · CNN readers shared their ups and downs of online dating. They agree: Expect the unexpected when you're involved with online dating. Some of these matches might lead AdExclusive African Dating Site. Join Today & Meet Your Match! Find Your Dream African Woman. View Photo Profiles. Join blogger.com by Cupid Media · Over Million Members · Backed by Cupid MediaService catalog: Instant Messaging, Send Interest, Make Connections ... read more

Any tips for responding to a first message? There are several significant findings from this empirical work. First, I situate the model in a high-stakes setting. For a registered dating user, there are about six NUTs on average eg, personal information, self-description, lifestyle, expected perfect match, personal ad and photo uploading to be completed before the service provider offers matching suggestions. To facilitate this, an introduction with an illustration was used and the use of Chinese language was kept as clear as possible for ease of understanding.

If someone isn't on board with those basic ideas, I don't want to spend eternity explaining feminism to my partner. Figure 1 illustrates the model of anticipatory emotions on volitions: a personal stake is the primary antecedent of emotions. Volitions have three dimensions. Article Google Scholar Chiang, K. Most of the people I have met haven't been right for me, online dating ups and downs, but they've been nice guys and great fits for someone else. Second, despite the fact that PLS copes well with a smaller sample size, a larger sample would make the conclusions more convincing.

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